Drivers of the water use efficiency changes in China during 1982-2015

Water use efficiency of China's karst ecosystems: The effect of different ecohydrological and climatic factors

Water use efficiency (WUE) is an important indicator for understanding the coupled ecosystem carbon and water cycles. However, the effect and contributions of factors on WUE variations in China's karst ecosystems for different climatic conditions have not been extensively studied. Our studies on WUE variations of China's karst ecosystems from 2001 to 2021 based on evapotranspiration and net primary productivity (NPP) from Moderate-resolution imaging spectroradiometer revealed the contributions of soil moisture (SM), leaf area index (LAI), precipitation (P), temperature (T), vapor pressure deficit (VPD), and CO2 concentration (CO2). Results showed that the trend of WUE was similar to that of NPP in terms of the latitude, longitude, and elevation, and WUE started abruptly decreasing after an elevation >3000 m until it reached 0 at 4500 m. WUE was primarily "slightly increased" in the humid region (H) and "slightly decreased" in the semi-humid region (SH), arid and semi-arid regions (ASA), and Qinghai-Tibet plateau region (QTP). CO2 (0.34), LAI (0.60), P (0.58), and LAI (0.55) exhibited the strongest positive direct effects on WUE in H, SH, ASA, and QTP, while VPD exhibited the strongest negative direct effect. VPD (0.26), VPD (0.28), SM (0.47), and P (0.39) had the strongest positive indirect effect, while T (-0.24), T (-0.18), VPD (-0.35), and P (-0.03) had the strongest negative indirect effect on WUE. The positive contributions of WUE variations in H, SH, ASA, and QTP were dominated by T (47.96 %), CO2 (26.36 %), P (8.81 %), and CO2 (52.97 %), whereas the negative contributions were dominated by P (-7.95 %), LAI (-26.57 %), CO2 (-35.98 %), and VPD (-9.59 %), respectively. This study quantifies the spatial and temporal distribution patterns of WUE in China's karst ecosystems and the regional differences between the multiple ecohydrological factors, thereby facilitating in-depth understanding and effective regulation for the carbon and water cycles in karst ecosystems.

Effects of Human Activities on Evapotranspiration and Its Components in Arid Areas

With the increasing impact of human activities on the environment, evapotranspiration (ET) has changed in arid areas, which further affects the water resources availability in the region. Therefore, understanding the impact of human activities on ET and its components is helpful to the management of water resources in arid areas. This study verified the accuracy of Fisher's model (PT-JPL model) for ET estimation in southern Xinjiang, China by using the evaporation complementarity theory dataset (AET dataset). The ET and the evapotranspiration components (T:E) of six land-use types were estimated in southern Xinjiang from 1982 to 2015, and the impact of human activities on ET was analyzed. In addition, the impact of four environmental factors (temperature (Temp), net radiation (Rn), relative humidity (RH), and NDVI) on ET were evaluated. The results showed that the calculated ET values of the PT-JPL model were close to the ET values of the AET dataset. The correlation coefficient (R2) was more than 0.8, and the NSE was close to 1. In grassland, water area, urban industrial and mining land, forest land, and cultivated land, the ET values were high, and in unused land types, the ET values were the lowest. The T:E values varied greatly in urban industrial and mining land, forest land, and cultivated land, which was due to the intensification of human activities, and the values were close to 1 in summer in recent years. Among the four environmental factors, temperature largely influenced the monthly ET. These findings suggest that human activities have significantly reduced soil evaporation and improved water use efficiency. The impact of human activities on environmental factors has caused changes in ET and its components, and appropriate oasis expansion is more conducive to regional sustainable development.

Climate change enhanced the positive contribution of human activities to net ecosystem productivity from 1983 to 2018

Introduction

Accurate assessment of the net ecosystem productivity (NEP) is very important for understanding the global carbon balance. However, it remains unknown whether climate change (CC) promoted or weakened the impact of human activities (HA) on the NEP from 1983 to 2018.

Methods

Here, we quantified the contribution of CC and HA to the global NEP under six different scenarios based on a boosted regression tree model and sensitivity analysis over the last 40 years.

Results and discussion

The results show that (1) a total of 69% of the areas showed an upward trend in the NEP, with HA and CC controlled 36.33 and 32.79% of the NEP growth, respectively. The contribution of HA (HA_con) far exceeded that of CC by 6.4 times. (2) The CO2 concentration had the largest positive contribution (37%) to NEP and the largest influence area (32.5%). It made the most significant contribution to the NEP trend in the range of 435–440 ppm. In more than 50% of the areas, the main loss factor was solar radiation (SR) in any control area of the climate factors. (3) Interestingly, CC enhanced the positive HA_con to the NEP in 44% of the world, and in 25% of the area, the effect was greater than 50%. Our results shed light on the optimal range of each climatic factor for enhancing the NEP and emphasize the important role of CC in enhancing the positive HA_con to the NEP found in previous studies.

Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

This study aimed to evaluate the interaction of environmental variables and Water Use Efficiency (WUE) via multivariate analysis to understand the importance of each variable in the carbon–water balance in MATOPIBA. Principal Component Analysis (PCA) was applied to reduce spatial dimensionality and to identify patterns by using the following data: (i) LST (MOD11A2) and WUE (ratio between GPP-MOD17A2 and ET-MOD16A2), based on MODIS orbital products; (ii) Rainfall based on CHIRPS precipitation product; (iii) slope, roughness, and elevation from the GMTED and SRTM version 4.1 products; and (iv) geographic data, Latitude, and Longitude. All calculations were performed in R version 3.6.3 and Quantum GIS (QGIS) version 3.4.6. Eight variables were initially used. After applying the PCA, only four were suitable: Elevation, LST, Rainfall, and WUE, with values greater than 0.7. A positive correlation (≥0.78) between the variables (Elevation, LST, and Rainfall) and vegetation was identified. According to the KMO test, a series-considered medium was obtained (0.7 < KMO < 0.8), and it was explained by one PC (PC1). PC1 was explained by four variables (Elevation, LST, Rainfall, and WUE), among which WUE (0.8 < KMO < 0.9) was responsible for detailing 65.77% of the total explained variance. Positive scores were found in the states of Maranhão and Tocantins and negative scores in Piauí and Bahia. The positive scores show areas with greater Rainfall, GPP, and ET availability, while the negative scores show areas with greater water demand and LST. It was concluded that variations in variables such as Rainfall, LST, GPP, and ET can influence the local behavior of the carbon–water cycle of the vegetation, impacting the WUE in MATOPIBA.

Dynamics of the Evaporation of Intercepted Precipitation during the Last Two Decades over China

The evaporation of intercepted precipitation (Ei) is an important component of evapotranspiration. Investigating the spatial and temporal variations of Ei and its driving factors can improve our understanding of water and energy balance in the context of China’s greening. This study investigated the spatial and temporal variation of Ei across China during 2001−2020 using PML ET product with a temporal resolution of 8 days and a spatial resolution of 500 m. The results showed that Ei generally decreased from southeast to northwest, which was contributed by the coupled effect of precipitation and vegetation coverage variation across China. Generally, Ei showed an increasing trend over the last two decades with an average changing rate of 0.45 mm/year/ The changing rate varied greatly among different regions, with the most obvious change occurring in tropical and humid regions. Precipitation was the most important climatic factor driving the interannual change of Ei over the past two decades, with an average contribution rate of 30.18~37.59%. Relative humidity was the second most important climatic factor following precipitation. Temperature showed contracting contribution in different thermal regions. The contribution rates of NDVI and LAI followed a similar spatial pattern. Both the contribution rates of NDVI and LAI generally increased along the moisture gradient from east to west and generally increased from south to north.

Assessing Variations in Water Use Efficiency and Linkages with Land-Use Changes Using Three Different Data Sources: A Case Study of the Yellow River, China

The dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.